Python Plotting Histograms with matplotlib

Review Review:

Python provides a number of excellent packages for data presentation, including the matplotlib module, which makes it easy to create high-quality data packages such as line graphs, bar charts, scatter plots, and so on.
Regarding the matplotlib module, we have already learned the basic framework of matplotlib, as well as common methods.

matplotlib module basics: Initial knowledge of the matplotlib module and learning of common methods
Matplotlib Module Underlying Principles: The matplotlib module consists of three layers of details: the scripting layer, the artwork layer, and the back-end layer.
matplotlib module line plotting: Summarize the properties and methods associated with line graphs

Among the charts provided by the matplotlib module, besides line charts, which are the most used, bar charts are also the charts we use for daily data analysis.

Next, we'll start learning about the properties and methods associated with plotting bar charts.

1. Overview of bar charts

1.1 What is a bar chart

• A bar chart, also known as a bar graph, is a chart that uses the length of a rectangle as the variable data for statistical purposes
• Bar graphs are used to compare two or more types of
• There is only one bar graph with the length of the rectangle as the variable
• Bar charts can be displayed horizontally or in a multi-dimensional manner.

1.2 Scenarios for using bar charts

• Bar charts are suitable for analysis of smaller data sets
• For two-dimensional datasets, comparing only one dimension of data difference terms
• Visualize differences in data between individuals
• Expression of discrete time series

1.3 Steps in drawing a bar chart

• import module
• Prepare the data, you can use numpy/pandas to organize the data
• Calling () to draw a bar chart

1.3 Case presentation

This time, we analyze the annual sales of products within the last 5 years to show the

The data used for the cases are as follows:

import random

x_data = ["20{}surname Nian".format(i) for i in range(16,21)]
y_data = [(100,300) for i in range(6)]

Plotting bar charts

import  as plt

["-serif"]=['SimHei']
["axes.unicode_minus"]=False

for i in range(len(x_data)):
    (x_data[i],y_data[i])

("Volume Analysis")
("Year")
("Sales.")

()

2. Bar chart properties

2.1 Column color filling

• facecolor (fc) keyword
• color Keywords
• Color abbreviation:

• Format form: (r,g,b)
• Value range: 0~1

2.2 Shape tracing settings

Column Border Color

• edgecolor or ec

Column Border Styles

• linestyle or ls
• Line Style:

2.3 Width of shape border

• linewidth or lw
• Bar Chart Fill Styles

hatch: sets the fill style
Attribute values: {'/', '', '|', '-', '+', 'x', 'o', 'O', '.' , '*'} |

2.4 Scale labels

• tickle label: defaults to numeric labels

We add a border style of "--" to the first bar chart, add the specified rgb color, and fill the circle.

for i in range(len(x_data)):
   (x_data[i],y_data[i],color=(0.2*i,0.2*i,0.2*i),linestyle="--",hatch="o")

3. Stacked bar charts

• In a bar chart, we will be comparing two sets of data in the same category at the same time, so we need to draw a stacked bar chart
• bottom : the y-coordinate of the base of the bar, default value is 0

In the first section of the case, add a set of y-axis data all data as follows:

x_data = ["20{}surname Nian".format(i) for i in range(16,21)]
 y_data = list((100,300) for i in range(5))
 y2_data = list((100,300) for i in range(5))

Add the method one more time, adding the bottom attribute

 (x_data,y_data,lw=0.5,fc="r",label="Phone")
 (x_data,y2_data,lw=0.5,fc="b",label="Android",bottom=y_data)

4. Side-by-side bar charts

To control the position and size of each bar in a side-by-side bar graph you can use the width attribute

• width: set the width of each group of bars
• x-axis: the width of the x-axis should be set directly for each group as well

For example, to continue the transformation of the above case, we added the width attribute to bar1 and bar2, and then individually set the width of the x-axis side by side to 0.3

x_width = range(0,len(x_data))
x2_width = [i+0.3 for i in x_width]


(x_width,y_data,lw=0.5,fc="r",width=0.3,label="Phone")
(x2_width,y2_data,lw=0.5,fc="b",width=0.3,label="Android")

(range(0,5),x_data)

5. Horizontal bar charts

In the bar chart, sometimes you need to let the bar chart is placed horizontally to compare the difference, we need to use to thebarhmethodologies

• (y,width): draw a horizontal bar graph
• In conjunction with the above case, the barh method is used instead

x_data = ["20{}surname Nian".format(i) for i in range(16,21)]
  y_data = list((100,300) for i in range(5))
  y2_data = list((100,300) for i in range(5))

  x_width = range(0,len(x_data))
  x2_width = [i+0.3 for i in x_width]

  (x_width,y_data,lw=0.5,fc="r",height=0.3,label="Phone")
  (x2_width,y2_data,lw=0.5,fc="b",height=0.3,label="Android")
  (range(0,5),x_data)

  ()

  ("Volume Analysis")
  ("Year")
  ("Sales.")

  ()

6. Adding line bar charts

When we look at bar charts, we sometimes need to view them with the aid of a folded line

• utilization()Summary Methodology Line Chart
• simultaneous use()Display coordinate values
• When stacking diagrams, you need to calculate the relative positions of the fold lines
  

(x_data,y_data,color="pink",linestyle="--")

(x_data, y2_data+200, color="skyblue", linestyle="-.")

# Histograms
(x_data,y_data,lw=0.5,fc="r",width=0.3,label="Phone",alpha=0.5)
(x_data,y2_data, lw=0.5, fc="b", width=0.3, label="Android",alpha=0.5,bottom=y_data)

for i,j in zip(x_data,y_data):

   (i,j+0.05,"%d"%j,ha="center",va="bottom")

for i2,j2 in zip(x_data,y2_data):

   (i2,j2+180,"%d"%j2,ha="center",va="bottom")

7. Positive and negative bar charts

We need to use the Axes object to set the position of the axes

• first using()method creates the axes object
• Then useThe module calls set_position to set the position of the axes.
• set_position Sets the axis position point
• The spines[] option has "left"|"bottom"|"width"|"height"
• set_position value format is (position type, quantity); position type; "outward"|"axes"|"data"|; quantity: center->("axes",0.5), zero->("data",0.0)
  

y_data = (100, 300,5)
y2_data = (100, 300,5)

ax = ()
["bottom"].set_position(('data', 0))

(x_data,+y_data,lw=0.5,fc="r",width=0.3,label="Phone")
(x_data,-y2_data, lw=0.5, fc="b", width=0.3, label="Android")

for i,j in zip(x_data,y_data):

    (i,j,"%d"%j,ha="center",va="top")

for i2,j2 in zip(x_data,y2_data):

    (i2,-j2,"%d"%j2,ha="center",va="bottom")

Summary.
In this paper, we atplotlib module to learn in detail to draw a variety of bar chart markers related properties and methods, in the encounter need to visualize the difference between the discrete data points, we can use bar () or barh () to draw a beautiful chart.

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